Prognosticating is often associated with clairvoyants and crystal balls that use magic to predict future events. Yes, we also predict future events. Yet, the method we use for that could not be more of the opposite from the above. Also, we heard the question whether Prognostics is the same as predictive analytics more than once. In short, we often encounter ambiguity about the word Prognostics.

Be it in the US, Germany, Switzerland or East Asia - Prognostics is a novel tool for maintenance management anywhere and requires explanation. We introduce the Cassantec blog to make the concept of Prognostics better understood and write about actual projects to make it more tangible. Of course we will also inform you about the latest product updates and relevant company news.

Industry is by and large still managed with a 3.0 mindset. Here are 10 common objections from Industry 3.0 managers and some thoughts from an Industry 4.0 company:

#1: “I know we must start with Data Analytics. But first we need to get a grip on our data quality.”
Our reply: Your data will never be perfect. So, don’t wait for a day that will never come. Besides, how can you assess the quality of your data, if you don’t work with it? So, data analytics does not only generate new insight, but is just as much a walking stick towards understanding your data. You better start now, because there is no benefit from waiting.

#2: “Your solution looks compelling. But we don’t need it, because we have our experts who do that job since years.”
Our reply: Your experts are certainly good at diagnosing the current equipment condition, but they would benefit greatly from the leverage Data Analytics provides and the hitherto unavailable foresight into the future. View our solution like a Mr. Spock who supports your Capitan Kirk experts.

#3: “Our organization is not ready for advanced data analytics. We need to learn to walk before we learn to run.”
Our reply: How do you know when your organization will be ready, if you don’t engage advanced tools? And how do you get your organization prepared without exposing your people to Industry 4.0 topics? Learning by doing is what is required.

#4: “We have no malfunctions.” [note: maintenance records regularly speak of replacements, but not of malfunctions]
Our reply: If you had to replace components or parts, and especially if that happened on short notice or even unexpectedly, then this is a malfunction that caused additional cost and downtime. And this is also the case, if the event occurred due to regular tear & wear. The reason for our different perspective is that tear & wear can be prognosticated with Data Analytics and hence the consequential maintenance work be moved from unplanned to planned, thereby saving cost and improving uptime.

#5: “We have no downtime.” [note: it is important to check whether the term “downtime” is understood as “unplanned downtime” or “planned and unplanned downtime”]
Our reply: Congratulations! And, by the way, you are probably paying dearly for that uptime. Financially better approaches are available when you adopt Data Analytics as a basis for decision making.

#6: “12 years ago, we had a huge unexpected damage. If you can help us avoid that in future, we need you. Otherwise, we see no benefit from your solution.”
Our reply: That damage must have been terrible. How likely is it that it will ever occur again? [typical answer: “very unlikely”] So, what about the routine malfunction events where each single incident costs less, but where the total sums up to quite a lot? Shouldn’t these be addressed as well?

#7: “You can prognosticate malfunctions alright. But I didn’t learn anything new about my equipment.”
Our reply: We agree that you probably didn’t learn anything new about the mechanical or physical properties of your equipment. But Data Analytics is not only about insight, but also about foresight. Data Analytics does not always have to help with root cause analysis to be highly beneficial. Actually, experts usually have significant expertise in identifying root causes, i.e. generating insight, while they have limited prognostic capabilities, which is where Data Analytics can help the most.

#8: “If you can’t explain the full physical logic, I can’t accept these retrospective validations, even though they are correct. Because how could I be sure that the future prognoses will be correct?”
Our reply: Well, the prognoses have passed this retrospective validation test. Rather than waiting for perfection, which will never come, you should embark on the forward journey now, use the solution, become Industry 4.0 savvy and regularly re-assess the benefits the solution brings.

#9: “I don’t have enough resources for this.”
Our reply: That is indeed an issue many of our customers face, because your team is tasked with keeping the operations going, which usually is a full-time job. We suggest you lobby for the respective resources, because otherwise you will be cut off from the direction the industry is going in and your company will gradually be losing ground vis-à-vis its peers.

#10: “We have just had a reorganization.” / “We are in the midst of a reorganization.” / “We are planning a reorganization.”
Our reply: After the reorganization is before the reorganization. While this is painful, the market and competition aren’t waiting for you.

– 26 January – written by Moritz von Plate, CEO of Cassantec

Change Management Lies at the Heart of Any Industry 4.0 Initiative

Any Industry 4.0 project aims at change in an organization. And change is hard! The probably best book ever on change is "Switch: How to change things when change is hard" by Chip and Dan Heath.
Read it!
Here is the authors� Switch Framework:

– 22 January – written by Moritz von Plate, CEO of Cassantec

"Data Analytics does not tell me anything new"

I occasionally hear operators of industrial equipment say:
“Data Analytics doesn’t tell me anything that I didn’t know already about my equipment.”
And then they go on to say: “And that is why I don’t need malfunction prognoses, which are derived from Data Analytics”.

An analogy helps explain why this perspective is flawed:

Imagine you drive your car and you know that your brake pads are wearing off.
This effect is perfectly well understood.
Will you wait with the repair until your brakes are gone and you have crashed into another car,
claiming that this is no issue, because you understood the problem and knew it was going to happen eventually? Obviously not…

While it is an absurd thought to repair your brakes only after an accident,
this is exactly what many industrial equipment operators do.
They know the tear & wear process of equipment,
and yet they wait until the equipment has worn out and experiences a malfunction before they repair it.
And here is where data analytics can generate enormous benefits:

Yes, it does not provide new or deeper explanations around the “why”.
But it can prognosticate the “when”, thereby allowing operators to find the optimal point in time for
repair before a malfunction event. And such prognoses are actually new information for most equipment operators.

– 14 November – written by Moritz von Plate, CEO of Cassantec

Cassantec launches new product generation

Cassantec has released the new version 2.x of its prognostic solution, comprising a new generation of functions and features. Version 2.x enhances the efficiency of the solution configuration and validation process, and extends prognostic capabilities and accuracy.

Hungarian cartoonist Gergely Dudas, also known as Dudolf, challenged fans to find an egg disguised alongside a group of bunnies. The good news is, he really did hide an egg in the picture. You only have to look hard enough. But what if there were no egg hidden in the picture? Would you be able to come to that conclusion? Or would you maybe end up identifying some odd shape as an egg, triggering a false positive?

The analogy of this in Data Science for industrial applications are clever algorithms searching through streams of historical process and condition data to detect and define the proverbial Easter eggs, i.e. those data patterns that describe a malfunction event. However, typically, there are no (or, actually, too few) malfunction events hidden in the data histories! And the reason for this is that experts are paid a lot of money to avoid malfunctions. Hence, the clever algorithms cannot work!

To overcome that challenge, latest cutting-edge research works on combining the power of data science with the richness of expert systems. Precisely those experts that work on avoiding the malfunctions typically know how to define the relevant data patterns. This input from outside the realm of data science allows smart algorithms to be put to good use.

The result is the ability to detect and even prognosticate malfunction events, even if they have never left their traces in the data lake. In other words, you can now find Easter eggs in data that hardly look like Easter eggs. And then you will also recognize the egg in Dudolf’s picture: it is disguised between a pair of white rabbit ears in the second row on the left hand side.

Captain Kirk and Mr. Spock are perfect examples for how the combination of intuition and logic yields superior results. Captain Kirk would always consult Mr. Spock, counting on his impeccable fact-based reasoning. He would way the arguments and then make the best possible decision.

In industrial asset management, Cassantec takes the role of Mr. Spock. The transparent, impartial, repeatable, logical and fact-based prognoses are input, if not foundation, for the decisions of asset and reliability managers, the Captain Kirks of industrial operations.

Or, like a customer of mine once said: “Let’s view Cassantec like another person in the room to help with making decisions.”

So, if you are still lacking a Mr. Spock at your side, please approach us!

And let’s hope that your Mr. Spock will never have to say: “Your logic was impeccable, Captain. We are in grave danger.”

– 01 September – written by Moritz von Plate, CEO of Cassantec

What can weather proverbs (“Bauernregeln”) teach us about heuristics and data analytics?

Probably the best known weather proverb (“Bauernregel”) in all of Germany is the “Siebenschläfer”. It goes:

"Regnet's am Siebenschläfertag, so regnet's sieben Wochen danach"

– if on June 27 there is rain, there will be rain for the following seven weeks.

This heuristic is based on century-old weather observations. And indeed, just around that time of the year, the jet stream settles geographically. And depending on whether it settles further north or south, it has an impact on the summer’s weather pattern.

But what hardly anybody in Germany knows is that the rule’s accuracy varies strongly by region:

•In Munich it has an accuracy of around 80%

•Further north in Berlin, the accuracy is in the high 60s

•In Hamburg, the rule does not work

And the proverb is from the south of Germany. Hence, it is hardly surprising that this is where it actually works. But ask a person from northern Germany (like my mother), and she will happily quote the “Siebenschläfer”-rule, not knowing that it is not applicable to her local weather.

Luckily, we nowadays all have 2 or 3 weather apps on our smartphone that can actually fill the void and provide much more accurate forecasts based on data analytics. So, we don’t have to rely on proverbs and heuristics anymore, of which we don’t know whether they actually apply.

Transfer that to industrial applications:

Many a maintenance strategy still works based on heuristics. For example: “Based on decades of experience, the turbine needs maintenance after 3 years of operation. The scope of required repairs increases dramatically, if we prolong that to 4 years.”

But is that heuristic experience still applicable after the dramatic changes of the last years? Don’t renewables lead to new operating regimes, new loads on the equipment and changed aging processes?

Doesn’t the increased cost pressure lead to a new cost optimum?

Nobody really knows.

I recommend: rather than relying on possibly false experience, use data analytics and be on the safe side!

The T-model of data analytics: how come that “predictive analytics” doesn’t predict the future?

In data analytics, both
depth and
breadth are desirable but can’t be had from the same tools.

Therefore, we follow the
T-model of data analytics. While there are many companies focusing on depth (the vertical bar in the “T”), Cassantec delivers the missing breadth (the horizontal bar in the “T”).

the when-questions. Here, diagnostic depth is complemented by prognostic breadth.

One leading provider of depth recently wrote: “XXX is often classified in the equipment reliability world as a “predictive analytic” product. However similar to other technologies in this space, it doesn’t “predict” the future, it basically finds deterioration in equipment performance well before it’s obvious, with very high confidence, and across many, many types of equipment. […] What we’ve seen over the years, is that while XXX does find the kinds of issues that could lead to catastrophic failure, it also finds hundreds of other problems, many that no Failure Mode Effects Analysis (FMEA) or standard maintenance program would anticipate.”

This is a great example of analytical depth, which is somewhat blurred by the term “predictive”.

Such outstanding depth must be complemented by Cassantec’s breadth in order for asset managers to have insight as well as foresight, both of which are required to optimally deploy their equipment. We are unrivalled in calculating explicit prognostic horizons over weeks, months, and in cases years (for any given time in the future we calculate the risk of malfunctions occurring).

So, adopting the T-model wasn’t only central for manufacturing 100 years ago. It is also paramount to lifting today’s asset management to the next level.

– 10 June – written by Moritz von Plate, CEO of Cassantec

"We have similar prognostic tools ourselves" - really?

Regularly, I hear from potential customers that they or their current services provider have tools that provide prognostic information. Really?

I suggest you ask the following probing questions:

•What is the prognostic horizon of these tools?

[if it is not weeks and months, it is not prognostic!]

•How explicit is the prognosis?

[if it is just an early warning (“beware, malfunction x is about to happen”) rather than actual

information on when to expect the malfunction, it is a better condition monitoring system,

but not prognostic!]

•Does it cover all relevant components and malfunctions drawing on all available process and

condition data, e.g. also lubricant analyses?

[if it covers just a subset of components, malfunctions and/or data, it is nice for the covered

portion, but not capable of delivering the full benefit of prognostics!]

If, on the other hand, the above questions can be answered satisfactorily, congratulations!

....and please let me know, because I would love to get a first-hand impression on that competitor of ours....

– 20 May – written by Moritz von Plate, CEO of Cassantec

The Use Cases of Prognostics

Somebody recently asked me: “why would I want to know when my equipment will develop a malfunction?” Well, until that moment, I thought it pretty obvious that having a peak preview into the future is rather helpful in many ways.

That question triggered me to make a list of the many use cases for knowing the future in industrial and equipment operations. Here it is:

Use case category “Maintenance and Repair”:

1.Long-term scheduling of maintenance

(Optimize scheduling and scoping of outages to secure high availability with minimum budget)

Deployment of a Prognostic Asset Management Solution for CCI’s Power Generation Assets

Background: Castleton Commodities International (CCI) is a leading global merchant energy company, trading energy commodities and operating a variety of energy assets. These include dual-fired (oil & gas) generating units and cogeneration units in the USA, ranging from 77 MW to 600 MW and covering peak demand in metro-politan areas such as Dallas and New York City. All generating assets are fully equipped with condition monitoring and diagnostic systems, recording and archiving condition and process data for all crucial asset components.

As a power generator primarily focused on commercial operations CCI seeks full transparency of both market and generation risk, which is largely driven by the risk of unscheduled generating unit downtime. To better understand and manage generation risk on the basis of their assets’ actual conditions, CCI has introduced Cassantec’s prognostic solution across its entire fleet of generating assets, including Roseton in Figure 1. With the acquisition of additional generating assets, the solution is gradually expanding.

Objective: Objective: CCI’s objective is to actively manage the future availability of its power generating assets, in line with its ongoing commercial commitments, and to secure power supply in upcoming peak demand periods. The prognostic solution, in particular, is to provide a daily update of unscheduled future downtime risk for its generating assets. The risk of unscheduled downtime is computed at the component level, then aggregated to the unit and fleet level, and explicitly compared to power-zone-specific market prices. For units that are “in the money” at any future point in time, risk-mitigating action is taken if necessary. Such action includes preemptive yet informed component maintenance or replacement, ideally during idle periods or scheduled revision cycles.

Approach: Approach: The prognostic solution provided by Cassantec is computing future risk profiles at component, unit, and fleet level, based on condition and process data of the generating assets. This data includes cur-rent and historical parameter values for all crucial asset components, such as gas and steam turbines, power generators and transformers, boilers and HRSGs, boiler feed pumps, induced and forced draft fans. In a first step, the prognostic solution uses current and historical condition and process data to project the component’s condition into the future. In a second step, the future condition is correlated with component-specific malfunction modes, to determine the future malfunction risk for all components considered. In a third step, the malfunction risks are illustrated and aggregated in a prognostic report that offers compo-nent, unit, and fleet level views, as displayed in Figures 2-4. The generation risk reports are complemented with market power price forecasts to determine both megawatt@risk and margin@risk indices. Monitoring these indices allows adjustment of trading and hedging strategies, mitigating both market and generation risk of the power business.

Benefits: CCI is expecting benefits of the prognostic solution on three levels:

Competitive commercial advantages at the fleet level, through the use of the prognostic reports for informed commitments in power trading and hedging

Higher uptime records at the unit level, through the use of the prognostic reports and related down-time risk profiles for improved maintenance planning, scheduling, and scoping

Lower asset management costs at the component level, through informed mitigation of perfor-mance flaws, inefficiencies, and latent defects and through targeted work order preparation

For reliability managers and mechanical engineers onsite, availability forecasts are a strong complement to the condition monitoring and diagnostic systems in place, consolidating data from different sources, ex-tending insights by an explicit future time dimension, and rendering standardized and conclusive reports. The forecasts may also serve as a shared planning tool in collaboration with equipment vendors, service providers, or insurers.

Next Steps: Continuous refinement of the operational solution, and extension to newly acquired generating assets.

Background: The Eurasian Natural Resources Corporation (ENRC) operates a
diversified portfolio of natural resource assets worldwide, including iron
ore, copper, and cobalt mines in Africa. Due to increasing cost pressure on
its mining operations, ENRC seeks to strengthen its asset management through
innovative and efficient digital solutions. In early 2015, ENRC configured,
tested, and deployed Cassantec’s prognostic solution at its Frontier Mine in
Katanga, DRC, seen in the upper picture. In a first release, the solution scope
comprised the mine’s main crusher, two cyclone pumps, and the SAG mill. In a
second release, the operator is adding the ball mill, pebble crushers, and
conveyors.

The
prognostic solution is based on condition and process data recorded for all
crucial mining assets. This data includes load, vibration, and lubricant
data, recorded by Cassantec’s partner WearCheck onsite in monthly intervals,
as well as vibration and temperature data taken from the operator’s online
monitoring system.

Objective: ENRC is seeking an accurate, consolidated, and
transparent reporting solution at the core of its mining asset management,
providing substantial insight and foresight for strategic and operational
decisions. Given equipment-specific maintenance plans with much flexibility
around schedule and scope, the operator wishes to minimize maintenance cost
and effort at constant levels of reliability and availability. While ENRC is
initially using the solution on a stand-alone basis, the prognostic reports
will be rendered through IBM Maximo at a later stage.

Approach: ENRC is applying Cassantec’s
prognostic solution for periodical reporting of future malfunction risks and
maintenance needs. In a first step, the solution forecasts equipment
conditions on the basis of current and historical condition data subject to
various operational scenarios. In a second step, future conditions are
correlated to equipment-specific malfunction modes, yielding end-of-life
forecasts. In a final step, prognostic reports are generated on the basis of
end-of-life forecasts, as illustrated in the lower picture. The reports are computed
at component level and then aggregated, supporting collective asset
management decisions.

Benefits: ENRC is targeting
benefits in three main areas:

Reduction of Downtime Cost: By
avoiding lost production from unscheduled delays and by bundling
maintenance tasks based on malfunction risk profiles

Reduction of Maintenance Cost: By
extending scheduled maintenance intervals at constant availability and
safety levels, and by better preparing for maintenance and replacement
tasks

Transparent Decision Basis: By
integrating condition data from different sources, and aggregating
insight and foresight at different management levels

Next Steps: The prognostic solution is
currently being used at ENRC’s Frontier Mine for the aforementioned types of
mining equipment with monthly updates of prognostic reports. The operator is
considering an extension of the solution to further equipment, as well as a
roll-out of the solution to further mining assets.

– 28 October – written by Moritz von Plate, CEO of Cassantec

The Weather Forecast and Risk Literacy

„There is a 60% chance of rain tomorrow.“

That is the type of information we are getting when looking at our weather app. One might be inclined to shout out: “I don’t want this probability, I want to know whether it will rain or not!”

Despite this apparent lack of relevance, why are we still using weather apps that give us these probabilities? That is, because we have become intuitively risk literate when it comes to weather forecasts.

When I was young, I was told that when the swallows are flying high, the weather will be good. And when they are flying low, it will rain. That heuristic forecast meant that we were soaked during many BBQs, while others never happened despite perfect sunshine.

Nowadays, when we are planning a BBQ, we check our weather app. And it will mention the probability of rain in the coming days. Intuitively, we have become accustomed to incorporate that information into our decisions: At a 60% chance we will probably organize a tent, at an 80% chance we will probably cancel the BBQ and go to the movies instead and at 20% we will probably simply take the risk. If we only want to go for a hike in the nearby park, our decisions will be different, but still be influenced by these probabilities.

In terms of risk analysis, we intuitively consider the following equation:

Risk = Likelihood x Impact.

Without knowing any statistics, I could imagine that users of weather apps make fewer planning mistakes, because they understand this equation. Meanwhile,
many operators of industrial assets are still, figuratively speaking, looking at the swallows rather than making use of all the data to generate meaningful risk profiles. They rely on experience and expert assessments rather than on algorithms to compute risk. This means that regularly they get seriously wet, e.g. when a critical gearbox breaks, while their risk-literate colleagues were prepared and have found shelter.

When will these operators will be ready to apply their risk literacy to their professional life?

– October 6 – written by Moritz von Plate, CEO of Cassantec

*** German version below ***

„It’s hard to make predictions, especially about the future!“

What do Niels Bohr, winner of the Nobel Prize in Physics, and Yogi Berra, Baseball Hall of Famer, have in common? The above quote is attributed to both of them.

…and Daniel Kahneman would certainly not disagree as we have seen in my last blog. Now we turn to the 2nd part of the blog: “Expert Intuition: When Can We Trust It?” – further quotes from Daniel Kahneman, „Thinking, Fast and Slow“ (2011), and a short observation by me in the context of the operation of industrial facilities.

Kahneman writes:

„Intuition as Recognition“: “[…] two basic conditions […]:

• an environment that is sufficiently regular to be predictable

•an opportunity to learn these regularities through prolonged practice

When both these conditions are satisfied, intuitions are likely to be skilled.”

“Indeed, the studies […] never produced a “smoking gun” demonstration, a case in which clinicians completely missed a highly valid cue that the algorithm detected.”

“If a strong predictive cue exists, human observers will find it, given a decent opportunity to do so. Statistical algorithms greatly outdo humans in noisy environments for two reasons: they are more likely than human judges to detect weakly valid cues and much more likely to maintain a modest level of accuracy by using such cues consistently.”

“Shortterm anticipation and long-term forecasting are different tasks […].”

“[…] they have not learned to identify the situations and the tasks in which intuition will betray them. The unrecognized limits of professional skill help explain why experts are often overconfident.”

Short observation:

The central question is whether the world of industrial assets meets both basic conditions as postulated by Kahneman such as to allow us to generally believe prognoses of the experts. The world of physical assets is certainly not as complex and volatile as the one of financial market experts, whose prognoses are notoriously useless. But our industrial world is probably also less repetitive as the world of fire fighters or nurses, whom Kahneman does grant a certain degree of learnability of prognoses.

I cannot make a final judgment call to what extent both of Kahneman’s conditions are met.

•Nevertheless, regarding the first condition of sufficient regularity: the increasing complexity along two dimensions (time, content) means that the validity of expert statements meets tight boundaries. An example: the energy transition puts pressure on power plant operators from two directions – while, on the one hand, margins are shrinking and often already negative, the operating demands, on the other hand, are on the rise. The fluctuations in the grid caused by sun and wind power need to be balanced by a load-dependent mode of operation. The impact of that on the life time of plant components is largely unknown such that availability prognoses by experts increasingly resemble a glance into the crystal ball. There are many such examples. They show that the traditional method of expert prognoses delivers insufficient results.

•And regarding the second condition of prolonged practice: in our aging society many companies suffer from a demographically induced loss of experience. This can only partially be mitigated due to cost pressures and the lack of younger successors.

Our take-away is:The intuition of experts needs the support from algorithms to ensure that Niels Bohr’s statement (or was it Yogi Berra after all?) will finally be remembered as what it really is – a bon mot!

Daniel Kahneman: “to maximize predictive accuracy, final decisions should be left to formulas“

Important topics should be left to intelligent persons. That is why I share quotes by Daniel Kahneman, winner of the Nobel Prize in Economics, about his observations on experts vs. algorithms that he made during decades of research.

About the quality of prognoses by experts:

“The experts performed worse than they would have if they had simply assigned equal probabilities to each of the three potential outcomes. [...] Even in the region they knew best, experts were not significantly better than nonspecialists.”

“About 60% of the studies have shown significantly better accuracy for the algorithms. The other comparisons scored a draw in accuracy, but a tie is tantamount to a win for the statistical rules, which are normally much less expensive to use than expert judgment. No exception has been convincingly documented.”

“Several studies have shown that human decision makers are inferior to a prediction formula even when they are given the score suggested by the formula!”

About the reasons for the lack of quality of experts’ prognoses:

“They feel that they can overrule the formula because they have additional information about the case, but they are wrong more often than not.”

“Another reason for the inferiority of expert judgment is that humans are incorrigibly inconsistent [...]. When asked to evaluate the same information twice, they frequently give different answers. The extent of the inconsistency is often a matter of real concern.”

A little later: “The brief pleasure of a cool breeze on a hot day may make you slightly more positive and optimistic about whatever you are evaluating at the time.” “Formulas do not suffer from such problems. Given the same input, they always return the same answer.”

About the reaction of the experts:

“We knew as a general fact that our predictions were little better than random guesses, but we continued to feel and act as if each of our specific predictions was valid.” (“illusion of validity”)

Cognitive illusions can be stubborn: “when my colleagues and I [...] learned that our […] tests had low validity, we accepted that fact intellectually, but it had no impact on either our feelings or our subsequent actions.”

“Tetlock also found that experts resisted admitting that they had been wrong, and when they were compelled to admit error, they had a large collection of excuses [...]. Experts are just human in the end.”

“They know they are skilled, but they don’t necessarily know the boundaries of their skill.”

Take away:

“The research suggests a surprising conclusion: to maximize predictive accuracy, final decisions should be left to formulas […].”

“if a test predicts an important outcome with a validity of .20 or .30, the test should be used.”

“Fortunately, the hostility to algorithms will probably soften as their role in everyday life continues to expand.”

******************

This was part 1 on Daniel Kahneman‘s findings.

To be continued with part 2: “Expert Intuition: When Can We Trust It?” – further quotes from Daniel Kahneman, „Thinking, Fast and Slow“ (2011), and a short discussion by me in the context of the operation and maintenance of industrial facilities.

Internet of Things: the Technical Sophistication of Applications Must Cope with Low Tech Realities

This is Cassantec’s CTO recently near a copper mine in the DR Congo trying to get a signal:

His experience shows that the reality is often-times much more low-tech and driven by legacy systems than IoT evangelists would like it to be. And in order to succeed in this world, solutions need to cope with that.

Through our SaaS approach, Cassantec has managed to couple state-of-the art algorithms and software with tried-and-true IT-features, linking our Prognostic solution seamlessly into the reality of the industrial world.

While the core solution is written in Scala and Java comprising highly advanced stochastic algorithms and data-analysis techniques, its interfaces are cunningly simple and standard:

-Data input: rather than linking into APIs and retrieving data, Cassantec has adopted a push-approach. Data batches are periodically exported from the customer’s database (typically automatically using small programs, but sometimes also manually) and transferred to Cassantec through several different means (typically upload to a SSL-secured ftp-server, but sometimes also manually through e-mail or on storage devices like CD ROM). The simpler the data format the better it is; .csv or .txt files are best, but all machine-readable formats are accepted.

-Results output: our calculation results are stored in standard JSON files (.txt-format). These can be fed into Cassantec’s proprietary html-based report format or any other format, ranging from legacy binary files or Excel to sophisticated Asset Management software. That way, users accustomed to their preferred ways can benefit from the advanced Prognostic Reports without having to install new and expensive IT (hard- or software).

-It goes without saying that Cassantec’s HTML5/JS-based reports are compatible with current and legacy browser versions.

-Furthermore, the often slow and intermittent access to the Internet at industrial facilities has been taken into consideration in the solution architecture. Results can be cached and used offline and the need for data transfers has been reduced to a minimum.

Through the means outlined above, Cassantec is ready to meet its customers’ needs for state-of-the-art insight, while being simple to implement, it is also flexible and mobile, without having to rely on the latest and greatest the IT-world has to offer.

– 16 July – written by Moritz von Plate, CEO of Cassantec

Human vs. Machine: Is SkyNet Coming?

The world is abuzz with discussions about humans vs. machines. The common wisdom is that the machine will replace humans in many professions. One could say: "digital eats [add any profession you can think of]". That may be true. But I am no prophet; and that is why I won't go out on a limb and make a judgment call as to when and to which extent this will be the case.

However, I do say that for the foreseeable future it shouldn't be "digital eats...", but rather "digital changes…”. It changes the way humans make decisions.

So far, so good…

But what does this mean specifically? Here are three hypotheses as to what is going on:

1)Many decisions are far too complex to be fully digitized in the near future.

2)People can get a great deal of decision support from machines.

3)People are beginning to embrace the change.

Hypothesis 1: complex decisions

Conferences about “digital this” and “digital that" all feature at least one speaker who describes situations where algorithms analyze data at lightning speed and automatically make decisions, for which humans neither have the computational power nor speed. That is certainly feasible in cases of high data volumes, yet rather limited complexity in the decision alternatives. A good example are forecasts how many bananas are likely to be sold in which supermarket.

This paradigm hits a wall in more complex situations where cause and effect and the resulting range of options to decide from are less obvious. An increasing vibration of a pump shaft can have many root causes (e.g. the process causes the pump to run dry, and/or the operator tortures the pump with steady operation at 110% of capacity, and/or the lubricant is degrading, and/or it is the wrong lubricant, and/or the shaft has been poorly aligned during last maintenance, and/or anything else?) and, hence, a plethora of alternatives on how to react. My hypothesis is that an automatic decision as to
what to do when in order to mitigate the problem is not around the corner.

Picture 1: What works for bananas isn’t necessarily applicable to complex machines like this pump

However, modern algorithms can help humans understand the problem much better, thus preparing them for better decisions. And that brings us to the second hypothesis.

Hypothesis 2: decision support

To this day, many decisions are made based on experience and gut feel. Both are, in a way, implicit analysis algorithms that happen inside people’s brains, often unconsciously. Tools like Prognostics drag much of that implicit analysis out into the light where it becomes objective, comprehensive, transparent, comparable, and repeatable.

No human decision maker can claim all these characteristics. But by using machines to deliver these and combining the machine’s output with the human’s ingenious intuition and capability for complex event processing, the resulting decision can be greatly improved.

Hypothesis 3: embracing change

Yet the challenge is that people need to get used to such a new collaboration with machines. That is no easy task. I remember the days when we would ridicule anybody with a mobile phone. Now I am one of these people who text while walking, simultaneously asking an App for directions to my meeting. It seems that I have changed my attitude a great deal in these 20+ years.

Picture 2: The evolution of the mobile phone

And there are two main reasons for that: (1) it wasn’t a big leap from smoke signs to the latest smartphone, but rather a step-by-step evolution; and (2) it was the insight that change is inevitable, which is why I adopted a “just do it” mentality, learning along the way to truly appreciate the benefits.

When speaking to my customers, I increasingly observe the recognition that embracing the digital paradigm is inevitable as well as the understanding that an evolutionary approach is warranted. Recently, a manager of an industrial corporation said to me: “I am still not quite ready for this. But if we don’t start adopting data analytics tools now, we will be left behind in a few years. So, let’s go for it…”

And that is also my conclusion: Let’s go for it, because SkyNet is far away and the Terminator has only been back in the movies!

- 8 June – written by Moritz von Plate, CEO of Cassantec

Back To The Roots: Predictive vs. Prognostic Or What’s The Difference Anyway?

...

The terms “predictive” and “prognostic” are synonyms. Merriam-Webster is pretty clear in this regard: to prognosticate is “to foretell from signs or symptoms: predict" (http://www.merriam-webster.com/dictionary/prognosticate).

...

Bar any nuances, which are lost on me as a non-native speaker, people use these words interchangeably. So why then do we at Cassantec make such a fuss about differentiating these two?

We consider fore-knowing to indicate much more certainty than fore-telling.

...

When transferring this differentiation to our world of the Internet of Things, we emphasize that
companies using Cassantec Prognostics will actually know in advance what will happen when; they join the pride of Prognosticators. In contrast, companies relying on Predictive Analytics will obtain a lot less depth about the future. Rather than learning when a malfunction will occur they will merely be given an early warning that a malfunction will eventually hit them – just when remains painfully nebulous.

...

Therefore, while the differentiation seems to be hairsplitting at first, it does add a relevant degree of semantic differentiation, which we will continue to emphasize.

...

- 1 June - written by Moritz von Plate, CEO of Cassantec

Demonstrating Cassantec Prognostics – Easy, High-Benefit, Low-Risk

...

Testing new software solutions is a daunting task. The real value can usually only be assessed after cumbersome, costly and lengthy implementation projects. That creates a hurdle, sometimes an insurmountable one, for organizations to test new approaches.

...

The recent development of SaaS and cloud offerings has removed a lot of these hurdles. However, for many solutions that closely relate to the physical world, e.g. products from the fields of Predictive Maintenance or Predictive Analytics, a lot of the old hassle remains.

...

We at Cassantec have gone to great lengths to further reduce the effort of testing us out. The result is a smooth and efficient process to configure our SaaS-solution. These are the steps we take and information we need for our patent-pending configuration methodology:

...

1.Build data history: our experience is that most advanced equipment operators gather and archive a plethora of process and condition data; therefore, this step is usually already completed before we come on board.

2.Hand over historical data batch: this is truly low-tech according to today’s standards; we are happy to receive data histories in the csv format on a memory stick, on a CD Rom or uploaded to our ftp site.

3.Gather further information:
sufficient are a rough sketch of the equipment (certainly no P&ID or anything with similar detail) and an indication in the sketch where the data sources are located, e.g. where the sensors sit or where the lubricant samples a taken. Not a prerequisite, but a nice-to-have, are your internal warning & alarm levels for the different data sources.

4.Specify malfunctions:
for many types of equipment, e.g. many pumps, turbines, fans, compressors, boilers, generators or transformers, we know the typical malfunctions. One half-day workshop with the operator’s engineering, reliability and/or maintenance staff are enough to adjust these to the specific operation at hand. The effort for equipment where our experience is less advanced is not much larger, probably reaching 2 or max 3 half-day workshops.

5.Develop specific data parameters:
The same workshops, during which the malfunctions are specified, are used for the definition of the data parameters. Our extensive experience, coupled with the operators’ know-how, allow a finalization of this work-step in the same go together with step (4).

6.Automate data transfer:
Although not strictly required for a validation of the prognoses, an automatic data transfer is needed for periodical updates of the Prognostic Reports. This is usually accomplished with a standard data query and – very importantly – it does not require any integration of our software with your historian or other systems.

7.Discuss results, use forecasts:
with the completion of step (6), you are ready to verify the prognoses and use the advanced foresight for optimized operations & maintenance decisions.

...

Last, but not least, and to really make the point, these are the things we do
NOT need from you for a configuration of our solution:

•A systematic documentation of the knowledge about malfunctions, their relation to data and the interpretation of both (similar to the results delivered by RCM). This is especially valuable in organizations with an ageing workforce where critical know-how is about to retire.

•Gain valuable insight into the quality of the data and whether the data covers the relevant malfunctions or not. This facilitates a much more targeted approach towards tapping new data sources than usual.

•Gain a tangible perspective on the benefits (qualitative and quantitative) to be gained from data analysis.

...

But most importantly, of course, you are now ready to use Prognostics to reach superior maintenance and life cycle decisions based on hitherto unavailable foresight.

...

- 4 May - written by the Cassantec Team

Hydropower: Managing Remaining Useful Life (RUL) with Prognostics

...

The use of complex data analytics in order to control and improve processes is a crucial element of the Internet of Things (IoT). For maintenance and repair activities the use of Big Data analytics is likewise becoming increasingly important. With the help of Cassantec’s data-based Prognostic technology, the future condition of equipment can be determined and RUL can be managed proactively. This creates the foundation for an intelligent maintenance planning & operating strategy.

...

...

Creating transparency and objective information about the RUL and the future condition of the plant were the main goals of applying Cassantec Prognostics to a hydroelectric power plant in Switzerland.

...

The project results went beyond what was expected: Detailed prognoses of the future condition of nine critical components are being updated on a monthly basis. The first prognoses already revealed that a sharp increase in vibration of one particular generator bearing was mainly responsible for the limitation of the RUL of the entire asset. A scenario analysis determined the dependence of the vibration data on the operational regime. It showed that the current operation mode decreased the RUL of the plant below what was necessary to stay within the long-term plan of operations.

...

Based on this scenario analysis the asset operator decided to adjust the operating mode of the generator to extend the generator’s and therewith the entire power plant’s RUL.

...

- 1 April - written by the Cassantec Team

Follow The Routines And Thought
Processes in Your Plant OperationsYou Are Accustomed to

Next time you are making a decision ask yourself:

...

-
Am I making a forward-looking
decision?

-
What was my thought process to come to that
decision?

-
Which tools and information sources did I
use when making the decision?

...

In many cases, the answer
to the first question will be “yes”. When making yourself aware of the
thought process you just went through, you will identify moments when you
were weighing probabilities and expectations around future events. While
continuing to apply this thought process, you can consider supporting your
implicit probabilities and expectations with explicit hard facts, with
Prognostics.

...

You will probably also
have used data and information from various tools and systems. We all know
how we get accustomed to using our tools in certain ways and are very
reluctant to changing these. Cassantec Prognostics accommodates this by
offering to integrate the prognostic information into the front-end of your
current tools. This needs to be done once during solution configuration.
After that you can continue to use your tools while benefiting from the
additional prognostic information.

...

So, rather than adjusting
your approach to decision making, we advise you to adjust your usage of
helpful tools to your tried and tested habits, thus allowing you to put your
decisions on a sounder footing.

...

Cassantec Prognostics – it’s easy
and intuitive to use!

- 26 February - written by the Cassantec Team

Reliability
Engineers are Prognosticators

Any
equipment operator knows that much of his thinking revolves around the
future.

He
routinely makes decisions that refer to the future, or in other words, are
prognostic in nature.

For
instance, even with a fixed maintenance schedule, he decides…

...

1.when to schedule the next
maintenance intervention, while expecting the equipment torun fine until then;

2.how to scope and prioritize
tasks and assets in the next scheduled intervention;

3.whether and how to respond to
sketchy condition data that’s appearing on the monitoringscreens;

4.how to allocate time and
attention to the different assets, based on respective importance and perceived urgency of action.

...

Hence, in essence, Reliability Engineers, Equipment
Engineers and Plant Operators are Prognosticators.While everybody’s goal is to base decisions on sound
facts, information about the future is in scarce supply. Consequently,
Reliability Engineers, i.e. Prognosticators, have to make do with
compromises, because no prognostic information is available to
them:

...

-"Only” information about the
current condition of the assets is available;

-Even advanced “predictive”
diagnostics is a sophisticated way of assessing the currentassetcondition;

-They have to rely on fleet
statistics to make inferences about individual components;

-They follow OEM
recommendations;

-They rely on experienced gut
feel.

Clearly,
there is an information gap: no objective, data-based information about the
future is available!

There is
one company filling that information gap: Cassantec!

The
Cassantec PrognosticReports
provide the Prognosticators with the right set of information:

helping
Reliability Engineers do their job according to the highest professional
standards!

- 26 January - written by Moritz von Plate, CEO
of Cassantec

The Need to Collaborate

The world
is just at the beginning of an upheaval caused by the push of ‘digital’ into
the industrial space. While industry players have been deeply involved in IT
and software for decades – just look, as an example, at the complex
instrumentation & control programming – the recent trend is different and
new. Whether we call it the Industrial Internet, the Internet of Things, or
Industrie 4.0, it creates a great number of challenges and, even more so,
opportunities.

Players as
diverse as IT powerhouses, software giants, industrial OEMs, and Telecoms
are jockeying for position to build the future backbone of the industrial
landscape. We at Cassantec are agnostic to who and which model wins. But we
are clearly not indifferent to the fact that Prognostics will be a crucial
part of the picture.

At the same
time, we know of the breadth of solutions that needs to be developed behind
the façade of the current buzzwords. Many diverse building blocks will have
to be implemented to deliver the promise of the Digital Factory. Since we
will not be venturing too far off our Prognostic path, we are working on
getting involved with the providers of the necessary platforms. As a logical
consequence we have launched a partner strategy and are currently in talks
with a number of highly relevant players regarding cooperation, ranging from
supplier-buyer relationships to potentially even a “Cassantec Inside”
approach.

These are
exciting times and we are truly happy to be part of the game. Stay tuned for
more…

- 07 January - written by the Cassantec Team

The benefits of a data-driven maintenance
strategy

The list of benefits of using prognostic data analysis for
maintenance management is long. Benefits include:

Knowing when equipment will likely run into trouble

Proactive prevention of failures is enabled

The overall system knowledge is increased (the impact of
different operating modes on likelihood of malfunctions becomes transparent)

Unscheduled maintenance can be turned in to well-ahead
planned and scheduled maintenance

Intelligent bundling of maintenance work is enabled,
yielding overall less and only technically really warranted interventions

For more information on how to succeed with a data-driven
maintenance strategy you may read the recently published
blog article of SMRP (Society for
Maintenance and Reliability Professionals) about data-driven maintenance
strategies.

- 19 December - written by the Cassantec Team

From Reactive and Preventive
Maintenance to Prognostics – an Overview

Asset
management comprises a large array of approaches, methods, acronyms and the
like. With this article we are trying to provide a concise overview of core
aspects in the landscape. It is intended to be a living document and will be
updated occasionally based on recent developments, comments, and
discussions. We are looking forward to your feedback.

Within the
broader field of asset management, there are various approaches to
maintenance management, which – in many cases – are used side by side
depending on failure risk and criticality of the equipment:

Reactive Maintenance (“Run
to Failure”)

Reactive
maintenance depicts the simple method of fixing a problem after it occurred.
The condition of the equipment is not monitored systematically or
data-based.

For equipment
that is not mission critical and for which downtime is not costly and/or
there is enough redundancy to substitute equipment quickly, reactive
maintenance may be the way to go. Yet, in operations in which uptime is
critical, damages are costly; and where there is low redundancy, reactive
maintenance is unsuitable. Also, in case of catastrophic events in case of
malfunctions this method is clearly insufficient.

Preventive Maintenance

This method
is based on a predefined schedule of regular maintenance interventions. The
intervals usually follow meantime between failure (MTBF) estimates from
similar components or the OEM recommendations.

This method
is fairly easy to organize and yields better results in terms of
availability than reactive maintenance. It does not, however, prevent
malfunctions systematically. Maintenance might be performed too early when
there was not yet a technical need for it. Similarly it might have been
scheduled too late and failed to prevent a malfunctions. Actually, given
that hardly any equipment exactly behaves like the average, there is a high
likelihood of maintenance to be done too early or too late.

Condition-based Maintenance
(CBM)

CBM depicts
the method of performing maintenance when the technical need for it arises.
It is based on condition data of the equipment. Maintenance is performed
after one or more indicators show that the equipment is going to fail or its
performance is deteriorating. Using this information one can perform
maintenance at the right time in order to prevent a failure.

Predictive Maintenance (PdM)

For PdM data
are used to predict the future condition of equipment in order to indicate
when maintenance should be performed. PdM allows conducting maintenance when
technically needed. Thus, failures can be prevented and no maintenance is
performed when not necessary at this point in time.

The
definitions for CBM and PdM are based on their respective Wikipedia
articles. Our interpretation is that these definitions essentially describe
the same approach. Do you agree with our understanding?

The
approaches defined above are sorted by the required level of sophistication
regarding data analysis. We are grouping data analysis approaches into the
following three usage categories:

Condition Monitoring

Objective and
data-based real-time assessment of equipment condition. For this method the
condition of the equipment is monitored by data measurement tools. The data
are typically stored in a historian. The observed condition is taken into
consideration for maintenance decisions.

(Predictive) Diagnostics /
(Predictive) Analytics

Objective and
data-based analysis of current equipment condition. The term ‘predictive’ is
added when there are early warnings before a malfunction is reached, yet
without providing an explicit time horizon. Thus, insight into the current
condition as well as expectations about the future condition are taken into
consideration for maintenance decisions.

Prognostics

Objective and
data-based prognosis of future conditions with an explicit time horizon.
Specifically, the difference between Prognostics and Predictive Diagnostics
/ Analytics is that the latter opens a time window towards developing a
potential malfunction. Yet it remains unclear, how long it will take until
this time window closes again. Will it be in two hours and therewith
requires immediate attention? Will it be in two days, or two weeks or rather
two months? Prognostics, in turn, does not only open the time window by
giving an alert that a malfunction will come up. It gives, in addition, a
malfunction risk for any time in the future and therewith defines the risk
of continuing operation without a maintenance intervention. The information
about expected equipment conditions at any point in time in the future is
taken into consideration for maintenance decisions.

***

The following table shows which
approaches to data analysis are required for which approach to maintenance
management:

Condition
Monitoring

Diagnostics

Prognostics

Reactive (“Run to Failure”)

No or very
limited data analysis

No or very
limited data analysis

No or very
limited data analysis

Preventive

Helpful

Not required

Not required

CBM

Required

Required

Required

PdM

Required

Required

Required

While
Reactive maintenance rests on the assumption that no maintenance will be
conducted, the resulting need for data analysis is limited to making sure
that a failure is detected when it occurs.

Since
Preventive maintenance derives its schedule from set rules, it also only
makes limited use of data. Of course, given that failure should be avoided,
installing a Condition Monitoring system does make sense.

CBM and PdM
both require extensive data. Since they entail decisions about what
to do when, both diagnostic insight into the current equipment
condition and prognostic foresight into the future development of the
condition are required. We believe that predictive approaches (see above –
no explicit future time window) do not offer sufficient foresight.
Therefore, Prognostics (also above – explicit forecast when time window
closes) is needed.

***

During
discussions with partners and customers, we hear the term Prognostics used
in many different contexts. Here is an attempt at categorizing these:

The underlying methodologies for
making any kind of future inference come in a range of facets:

Process-related forecasts aremaintenance tools that help scheduling
and organizing maintenance processes. For instance work order management
systems help to procure the right equipment for certain interventions in
time based on information of past interventions. Further, the expected costs
and the associated work load can be estimated based on the past information.
While the information from these forecasts is highly relevant for asset
management, it does not help operators in their journey towards CBM / PdM.

Failure-based
forecasts require a history of past failures. Past data trends that are
associated with a failure are compared with current data. Using, for
example, Cox regression, Weibull fitting or physical modeling allows
forecasting future asset conditions. The problem with this approach is that
failure data are hardly available given their cost to obtain them.
Therefore, these approaches are limited to niche applications.

Condition-based forecasts also base their analyses on past and current data.
However, in contrast to failure-based forecasts, no failure history is
required in order to prognosticate failures. Instead, stochastic models are
used to detect data anomalies. These data anomalies, in turn, are related to
potential malfunctions, again using stochastic algorithms or expert
assessments. The result is a risk distribution over time (e.g. the risk of
malfunction x is 2% tomorrow, 5% in 10 days, …). Given that it is hard to
ensure objective, transparent, repeatable, and scalable expert assessments,
we prefer stochastic approaches.

***

Prognostics - ideal for assets
without a failure history that ought to remain without a failure history!

- 26 November - written by Julia Heggemann

Prognostics: the future is hidden in our past data

Be it in the US, Germany, Switzerland or East Asia - Prognostics is a novel tool for maintenance management anywhere and requires explanation. We introduce the Cassantec blog to make the concept of Prognostics better understood and write about actual projects to make it more tangible. Of course we will also inform you about the latest product updates and relevant company news.

We keep our first blog entry short and simple by answering the question:

What is Prognostics?

Prognostics provides insight into the future state of assets with an explicit time horizon of typically weeks or months, in special cases even years. While predictive analytics tools give early warnings opening a time window without providing any information on when it will close again, Prognostics focuses on specifying the future moment when the window closes, i.e. when the risk of malfunctions is too high. Thus, Prognostics is a tool created to optimize the maintenance management of industrial assets in order to increase their reliability and availability.

By using Prognostics you will learn when certain assets will likely run into trouble. The prognoses are done for each individual asset: that means it does not rely on averages derived from comparable assets but only on the assets actual data. This way the prognoses are highly accurate and have an excellent prognostic strength.

The Prognostics model uses stochastic methods and sophisticated algorithms. Historical data about the asset´s condition are fed into the model to forecast the availability and the risk of failure. An explicit risk profile shows how the failure risk of an asset develops over time.

The Prognostics model uses stochastic methods and sophisticated algorithms. Historical data about the assets condition are fed into the model to forecast the availability and the risk of failure. An explicit risk profile shows how the failure risk of an asset develops over time.

This way maintenance can be scheduled when really needed, that is not too early, but also before the asset is likely to cause problems. Prognostics also allows to detect malfunctions before they turn into a problem: a scenario analysis reveals the influence of for instance different load scenarios on the development of the malfunction risk. Based on experience even small adjustments in operations can prevent many malfunctions and therewith increase the asset´s Remaining Useful Live (RUL).

Cassantec is the first provider of Prognostics. Being complementary to diagnostics and predictive analytics, Prognostics can be used as a stand-alone data-analysis tool or to make the forecasts delivered by predictive maintenance really actionable.

To sum it up, Prognostics:

Delivers risk profiles about the future state of an asset

Uses stochastic methods with sophisticated algorithms

Is based on historical data of specific assets

Can be used as a stand-alone tool or complementary to predictive analytics

The goal of this first blog
entry was to make the concept of Prognostics clearer and to reduce the
ambiguity associated with the word. Details on certain aspects and case
studies will follow on a regular basis.